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Contributing

Thanks for contributing to PyKMExtract.

Scope

This project is intentionally conservative. Please prefer:

  • small, reviewable changes
  • explicit heuristics over opaque complexity
  • failure visibility over silent overfitting
  • opt-in AI enhancement over implicit behavior changes

Local Setup

Install in editable mode:

pip install -e .

Run tests:

python3 -m unittest discover -s tests -v

If you are working from source without installation:

PYTHONPATH=src python3 -m pykmextract.cli ...

Project Conventions

  • Keep the default extraction path simple and deterministic.
  • Add AI behavior only as an explicit enhancement path.
  • Preserve KM curves as step functions; do not smooth them into continuous curves.
  • Prefer relative links in documentation so the repository renders correctly on GitHub.
  • Keep difficult figures visible as manual-review or failure cases instead of forcing the default pipeline to fit them.

Code Style

  • Python 3.9+
  • Prefer small functions with clear boundaries.
  • Avoid adding duplicate public APIs when an existing result object already exposes the capability.
  • Add tests for every behavioral change.
  • Avoid broad architectural rewrites unless they clearly reduce complexity.

Real Figure Data

If you contribute new real KM examples, use predictable grouped filenames:

  • studyXX_full.png
  • studyXX_pfs.png
  • studyXX_os.png

Recommended supporting files:

  • images/literatures.md
  • data/real_km/notes/
  • data/real_km/semantic_seed/

Pull Requests

A good PR should include:

  • what changed
  • why it changed
  • what risks remain
  • which tests were run

If the change affects extraction quality, include at least one before/after example or point to the relevant files under runs/.